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  <h1>Source code for rl_coach.agents.categorical_dqn_agent</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Copyright (c) 2017 Intel Corporation </span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">Union</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">rl_coach.agents.dqn_agent</span> <span class="k">import</span> <span class="n">DQNNetworkParameters</span><span class="p">,</span> <span class="n">DQNAlgorithmParameters</span><span class="p">,</span> <span class="n">DQNAgentParameters</span>
<span class="kn">from</span> <span class="nn">rl_coach.agents.value_optimization_agent</span> <span class="k">import</span> <span class="n">ValueOptimizationAgent</span>
<span class="kn">from</span> <span class="nn">rl_coach.architectures.head_parameters</span> <span class="k">import</span> <span class="n">CategoricalQHeadParameters</span>
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">StateType</span>
<span class="kn">from</span> <span class="nn">rl_coach.exploration_policies.e_greedy</span> <span class="k">import</span> <span class="n">EGreedyParameters</span>
<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.prioritized_experience_replay</span> <span class="k">import</span> <span class="n">PrioritizedExperienceReplay</span>
<span class="kn">from</span> <span class="nn">rl_coach.schedules</span> <span class="k">import</span> <span class="n">LinearSchedule</span>


<span class="k">class</span> <span class="nc">CategoricalDQNNetworkParameters</span><span class="p">(</span><span class="n">DQNNetworkParameters</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">heads_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="n">CategoricalQHeadParameters</span><span class="p">()]</span>


<div class="viewcode-block" id="CategoricalDQNAlgorithmParameters"><a class="viewcode-back" href="../../../components/agents/value_optimization/categorical_dqn.html#rl_coach.agents.categorical_dqn_agent.CategoricalDQNAlgorithmParameters">[docs]</a><span class="k">class</span> <span class="nc">CategoricalDQNAlgorithmParameters</span><span class="p">(</span><span class="n">DQNAlgorithmParameters</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    :param v_min: (float)</span>
<span class="sd">        The minimal value that will be represented in the network output for predicting the Q value.</span>
<span class="sd">        Corresponds to :math:`v_{min}` in the paper.</span>

<span class="sd">    :param v_max: (float)</span>
<span class="sd">        The maximum value that will be represented in the network output for predicting the Q value.</span>
<span class="sd">        Corresponds to :math:`v_{max}` in the paper.</span>

<span class="sd">    :param atoms: (int)</span>
<span class="sd">        The number of atoms that will be used to discretize the range between v_min and v_max.</span>
<span class="sd">        For the C51 algorithm described in the paper, the number of atoms is 51.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">v_min</span> <span class="o">=</span> <span class="o">-</span><span class="mf">10.0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">v_max</span> <span class="o">=</span> <span class="mf">10.0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">atoms</span> <span class="o">=</span> <span class="mi">51</span></div>


<span class="k">class</span> <span class="nc">CategoricalDQNExplorationParameters</span><span class="p">(</span><span class="n">EGreedyParameters</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">epsilon_schedule</span> <span class="o">=</span> <span class="n">LinearSchedule</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_epsilon</span> <span class="o">=</span> <span class="mf">0.001</span>


<span class="k">class</span> <span class="nc">CategoricalDQNAgentParameters</span><span class="p">(</span><span class="n">DQNAgentParameters</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">algorithm</span> <span class="o">=</span> <span class="n">CategoricalDQNAlgorithmParameters</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">exploration</span> <span class="o">=</span> <span class="n">CategoricalDQNExplorationParameters</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">network_wrappers</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;main&quot;</span><span class="p">:</span> <span class="n">CategoricalDQNNetworkParameters</span><span class="p">()}</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="s1">&#39;rl_coach.agents.categorical_dqn_agent:CategoricalDQNAgent&#39;</span>


<span class="c1"># Categorical Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf</span>
<span class="k">class</span> <span class="nc">CategoricalDQNAgent</span><span class="p">(</span><span class="n">ValueOptimizationAgent</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">agent_parameters</span><span class="p">,</span> <span class="n">parent</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s1">&#39;LevelManager&#39;</span><span class="p">,</span> <span class="s1">&#39;CompositeAgent&#39;</span><span class="p">]</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">agent_parameters</span><span class="p">,</span> <span class="n">parent</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">v_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">v_max</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">atoms</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">distribution_prediction_to_q_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prediction</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">)</span>

    <span class="c1"># prediction&#39;s format is (batch,actions,atoms)</span>
    <span class="k">def</span> <span class="nf">get_all_q_values_for_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="p">:</span> <span class="n">StateType</span><span class="p">):</span>
        <span class="n">q_values</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="o">.</span><span class="n">requires_action_values</span><span class="p">():</span>
            <span class="n">q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_prediction</span><span class="p">(</span><span class="n">states</span><span class="p">,</span>
                                           <span class="n">outputs</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="o">.</span><span class="n">output_heads</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">q_values</span><span class="p">])</span>

        <span class="k">return</span> <span class="n">q_values</span>

    <span class="k">def</span> <span class="nf">get_all_q_values_for_states_and_softmax_probabilities</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="p">:</span> <span class="n">StateType</span><span class="p">):</span>
        <span class="n">actions_q_values</span><span class="p">,</span> <span class="n">softmax_probabilities</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="o">.</span><span class="n">requires_action_values</span><span class="p">():</span>
            <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="o">.</span><span class="n">output_heads</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">q_values</span><span class="p">,</span>
                       <span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="o">.</span><span class="n">output_heads</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">softmax</span><span class="p">]</span>
            <span class="n">actions_q_values</span><span class="p">,</span> <span class="n">softmax_probabilities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_prediction</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="n">outputs</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">actions_q_values</span><span class="p">,</span> <span class="n">softmax_probabilities</span>

    <span class="k">def</span> <span class="nf">learn_from_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
        <span class="n">network_keys</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">input_embedders_parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>

        <span class="c1"># for the action we actually took, the error is calculated by the atoms distribution</span>
        <span class="c1"># for all other actions, the error is 0</span>
        <span class="n">distributional_q_st_plus_1</span><span class="p">,</span> <span class="n">TD_targets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">parallel_prediction</span><span class="p">([</span>
            <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">target_network</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">next_states</span><span class="p">(</span><span class="n">network_keys</span><span class="p">)),</span>
            <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">states</span><span class="p">(</span><span class="n">network_keys</span><span class="p">))</span>
        <span class="p">])</span>

        <span class="c1"># add Q value samples for logging</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">q_values</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution_prediction_to_q_values</span><span class="p">(</span><span class="n">TD_targets</span><span class="p">))</span>

        <span class="c1"># select the optimal actions for the next state</span>
        <span class="n">target_actions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution_prediction_to_q_values</span><span class="p">(</span><span class="n">distributional_q_st_plus_1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">m</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">batch</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>

        <span class="n">batches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>

        <span class="c1"># an alternative to the for loop. 3.7x perf improvement vs. the same code done with for looping.</span>
        <span class="c1"># only 10% speedup overall - leaving commented out as the code is not as clear.</span>

        <span class="c1"># tzj_ = np.fmax(np.fmin(batch.rewards() + (1.0 - batch.game_overs()) * self.ap.algorithm.discount *</span>
        <span class="c1">#                        np.transpose(np.repeat(self.z_values[np.newaxis, :], batch.size, axis=0), (1, 0)),</span>
        <span class="c1">#                     self.z_values[-1]),</span>
        <span class="c1">#             self.z_values[0])</span>
        <span class="c1">#</span>
        <span class="c1"># bj_ = (tzj_ - self.z_values[0]) / (self.z_values[1] - self.z_values[0])</span>
        <span class="c1"># u_ = (np.ceil(bj_)).astype(int)</span>
        <span class="c1"># l_ = (np.floor(bj_)).astype(int)</span>
        <span class="c1"># m_ = np.zeros((batch.size, self.z_values.size))</span>
        <span class="c1"># np.add.at(m_, [batches, l_],</span>
        <span class="c1">#           np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (u_ - bj_))</span>
        <span class="c1"># np.add.at(m_, [batches, u_],</span>
        <span class="c1">#           np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (bj_ - l_))</span>

        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
            <span class="n">tzj</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">fmin</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">rewards</span><span class="p">()</span> <span class="o">+</span>
                                  <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">batch</span><span class="o">.</span><span class="n">game_overs</span><span class="p">())</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">discount</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
                                  <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
                          <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
            <span class="n">bj</span> <span class="o">=</span> <span class="p">(</span><span class="n">tzj</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
            <span class="n">u</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">bj</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
            <span class="n">l</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">bj</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
            <span class="n">m</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">+=</span> <span class="p">(</span><span class="n">distributional_q_st_plus_1</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">target_actions</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">u</span> <span class="o">-</span> <span class="n">bj</span><span class="p">))</span>
            <span class="n">m</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">u</span><span class="p">]</span> <span class="o">+=</span> <span class="p">(</span><span class="n">distributional_q_st_plus_1</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">target_actions</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">bj</span> <span class="o">-</span> <span class="n">l</span><span class="p">))</span>

        <span class="c1"># total_loss = cross entropy between actual result above and predicted result for the given action</span>
        <span class="c1"># only update the action that we have actually done in this transition</span>
        <span class="n">TD_targets</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">actions</span><span class="p">()]</span> <span class="o">=</span> <span class="n">m</span>

        <span class="c1"># update errors in prioritized replay buffer</span>
        <span class="n">importance_weights</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;weight&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="p">,</span> <span class="n">PrioritizedExperienceReplay</span><span class="p">)</span> <span class="k">else</span> <span class="kc">None</span>

        <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">train_and_sync_networks</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">states</span><span class="p">(</span><span class="n">network_keys</span><span class="p">),</span> <span class="n">TD_targets</span><span class="p">,</span>
                                                               <span class="n">importance_weights</span><span class="o">=</span><span class="n">importance_weights</span><span class="p">)</span>

        <span class="n">total_loss</span><span class="p">,</span> <span class="n">losses</span><span class="p">,</span> <span class="n">unclipped_grads</span> <span class="o">=</span> <span class="n">result</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>

        <span class="c1"># TODO: fix this spaghetti code</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="p">,</span> <span class="n">PrioritizedExperienceReplay</span><span class="p">):</span>
            <span class="n">errors</span> <span class="o">=</span> <span class="n">losses</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="n">batch</span><span class="o">.</span><span class="n">actions</span><span class="p">()]</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">&#39;update_priorities&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;idx&#39;</span><span class="p">),</span> <span class="n">errors</span><span class="p">))</span>

        <span class="k">return</span> <span class="n">total_loss</span><span class="p">,</span> <span class="n">losses</span><span class="p">,</span> <span class="n">unclipped_grads</span>

</pre></div>

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